MCF10A metabolism screen
Marion Hardy
2023-10-27
Introduction
Analysis of MCF10A in 16 different conditions 2 biological replicates per sample
This report was made following John’s suggestion to check the difference between the samples separated by PC1.
Create new coldata
Add to the metadata to group the PC1 left group (EGF Oligo and growth medium) vs all other samples.
PCA
All conditions
Heatmap of z score
Comparing the EGF+Oligo vs all others (excluding GM)
| x |
|---|
| Intercept |
| PC1_OlEGF_vs_others |
| PC1_out_vs_others |
| replicate |
Volcano plots
GSEA analysis (logFc)
GOBP
GOMF
Reactome
KEGG
Wikipathways
What effects comes from EGF and what comes oligo?
## [1] "Intercept" "PC1_sep_EGF_vs_others"
## [3] "PC1_sep_Oligo_vs_others" "PC1_sep_out_vs_others"
## [5] "replicate"Venn diagram of common genes
But this doesn’t take into account the logFc of the change in those genes Is there a way to score a pathway such as: - EGF explains 20% of the change in that pathway’s enrichment - Oligo explains the other 80%
GSEA
GOBP
Get the common terms and facet grid the different comparisons
Reactome
Get the common terms and facet grid the different comparisons
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
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## Matrix products: default
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## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] lubridate_1.9.0 timechange_0.1.1
## [3] ReactomePA_1.42.0 org.Hs.eg.db_3.16.0
## [5] AnnotationDbi_1.60.0 NMF_0.26
## [7] cluster_2.1.4 rngtools_1.5.2
## [9] registry_0.5-1 clusterProfiler_4.6.0
## [11] msigdbr_7.5.1 xlsx_0.6.5
## [13] pheatmap_1.0.12 ggrepel_0.9.2
## [15] biomaRt_2.54.1 cowplot_1.1.1
## [17] DESeq2_1.38.1 SummarizedExperiment_1.28.0
## [19] Biobase_2.58.0 MatrixGenerics_1.10.0
## [21] matrixStats_0.62.0 GenomicRanges_1.49.0
## [23] GenomeInfoDb_1.34.4 IRanges_2.32.0
## [25] S4Vectors_0.36.0 BiocGenerics_0.44.0
## [27] forcats_0.5.2 stringr_1.4.1
## [29] dplyr_1.0.10 purrr_0.3.5
## [31] readr_2.1.3 tidyr_1.2.1
## [33] tibble_3.1.8 ggplot2_3.4.0
## [35] tidyverse_1.3.2 RColorBrewer_1.1-3
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## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.2.0 RSQLite_2.2.19
## [4] grid_4.2.2 BiocParallel_1.32.4 scatterpie_0.1.8
## [7] munsell_0.5.0 codetools_0.2-18 withr_2.5.0
## [10] colorspace_2.0-3 GOSemSim_2.24.0 filelock_1.0.2
## [13] highr_0.9 knitr_1.41 rstudioapi_0.14
## [16] rJava_1.0-6 DOSE_3.24.2 labeling_0.4.2
## [19] GenomeInfoDbData_1.2.9 polyclip_1.10-4 bit64_4.0.5
## [22] farver_2.1.1 downloader_0.4 vctrs_0.5.0
## [25] treeio_1.22.0 generics_0.1.3 gson_0.0.9
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## [34] bitops_1.0-7 cachem_1.0.6 fgsea_1.24.0
## [37] gridGraphics_0.5-1 DelayedArray_0.23.2 assertthat_0.2.1
## [40] scales_1.2.1 ggraph_2.1.0 enrichplot_1.18.3
## [43] googlesheets4_1.0.1 gtable_0.3.1 tidygraph_1.2.2
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## [49] gargle_1.2.1 broom_1.0.1 BiocManager_1.30.19
## [52] yaml_2.3.6 reshape2_1.4.4 modelr_0.1.10
## [55] backports_1.4.1 qvalue_2.30.0 tools_4.2.2
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## [94] DBI_1.1.3 tweenr_2.0.2 dbplyr_2.2.1
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## [100] Matrix_1.5-1 cli_3.4.1 parallel_4.2.2
## [103] igraph_1.3.5 pkgconfig_2.0.3 xml2_1.3.3
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